Overview

Dataset statistics

Number of variables47
Number of observations59477
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 MiB
Average record size in memory159.0 B

Variable types

Numeric15
Categorical1
Boolean31

Alerts

rent_approval_date is highly overall correlated with monthly_rent and 2 other fieldsHigh correlation
floor_area_sqm is highly overall correlated with flat_type_model and 1 other fieldsHigh correlation
lease_commence_date is highly overall correlated with region_central regionHigh correlation
latitude is highly overall correlated with town_importance and 12 other fieldsHigh correlation
longitude is highly overall correlated with region_east region and 10 other fieldsHigh correlation
monthly_rent is highly overall correlated with rent_approval_date and 1 other fieldsHigh correlation
distance_to_nearest_existing_mrt is highly overall correlated with town_marine paradeHigh correlation
distance_to_nearest_planned_mrt is highly overall correlated with region_central region and 5 other fieldsHigh correlation
coe_price_indicator is highly overall correlated with rent_approval_date and 2 other fieldsHigh correlation
stock_price is highly overall correlated with rent_approval_date and 1 other fieldsHigh correlation
town_importance is highly overall correlated with latitude and 5 other fieldsHigh correlation
town_centrality_page_rank is highly overall correlated with region_central region and 19 other fieldsHigh correlation
flat_type_model is highly overall correlated with floor_area_sqm and 1 other fieldsHigh correlation
flat_type is highly overall correlated with floor_area_sqm and 1 other fieldsHigh correlation
region_central region is highly overall correlated with lease_commence_date and 4 other fieldsHigh correlation
region_east region is highly overall correlated with longitude and 3 other fieldsHigh correlation
region_north region is highly overall correlated with latitude and 6 other fieldsHigh correlation
region_north-east region is highly overall correlated with latitude and 2 other fieldsHigh correlation
region_west region is highly overall correlated with longitude and 2 other fieldsHigh correlation
town_ang mo kio is highly overall correlated with latitude and 1 other fieldsHigh correlation
town_bedok is highly overall correlated with longitude and 2 other fieldsHigh correlation
town_bukit batok is highly overall correlated with longitude and 1 other fieldsHigh correlation
town_bukit merah is highly overall correlated with latitude and 1 other fieldsHigh correlation
town_central is highly overall correlated with town_importanceHigh correlation
town_choa chu kang is highly overall correlated with town_centrality_page_rankHigh correlation
town_clementi is highly overall correlated with town_centrality_page_rankHigh correlation
town_geylang is highly overall correlated with town_centrality_page_rankHigh correlation
town_hougang is highly overall correlated with town_centrality_page_rankHigh correlation
town_jurong west is highly overall correlated with longitude and 2 other fieldsHigh correlation
town_kallang/whampoa is highly overall correlated with town_importance and 1 other fieldsHigh correlation
town_marine parade is highly overall correlated with distance_to_nearest_existing_mrtHigh correlation
town_punggol is highly overall correlated with latitudeHigh correlation
town_queenstown is highly overall correlated with latitude and 1 other fieldsHigh correlation
town_sembawang is highly overall correlated with latitude and 1 other fieldsHigh correlation
town_sengkang is highly overall correlated with latitude and 2 other fieldsHigh correlation
town_tampines is highly overall correlated with latitude and 4 other fieldsHigh correlation
town_toa payoh is highly overall correlated with distance_to_nearest_planned_mrt and 1 other fieldsHigh correlation
town_woodlands is highly overall correlated with latitude and 4 other fieldsHigh correlation
town_yishun is highly overall correlated with latitude and 3 other fieldsHigh correlation
town_ang mo kio is highly imbalanced (68.0%)Imbalance
town_bedok is highly imbalanced (67.0%)Imbalance
town_bishan is highly imbalanced (83.7%)Imbalance
town_bukit batok is highly imbalanced (77.0%)Imbalance
town_bukit merah is highly imbalanced (70.4%)Imbalance
town_bukit panjang is highly imbalanced (82.1%)Imbalance
town_bukit timah is highly imbalanced (98.9%)Imbalance
town_central is highly imbalanced (91.9%)Imbalance
town_choa chu kang is highly imbalanced (77.9%)Imbalance
town_clementi is highly imbalanced (76.9%)Imbalance
town_geylang is highly imbalanced (81.3%)Imbalance
town_hougang is highly imbalanced (72.3%)Imbalance
town_jurong east is highly imbalanced (81.3%)Imbalance
town_jurong west is highly imbalanced (62.0%)Imbalance
town_kallang/whampoa is highly imbalanced (80.2%)Imbalance
town_marine parade is highly imbalanced (91.9%)Imbalance
town_pasir ris is highly imbalanced (81.2%)Imbalance
town_punggol is highly imbalanced (73.5%)Imbalance
town_queenstown is highly imbalanced (78.8%)Imbalance
town_sembawang is highly imbalanced (83.6%)Imbalance
town_sengkang is highly imbalanced (64.6%)Imbalance
town_serangoon is highly imbalanced (84.9%)Imbalance
town_tampines is highly imbalanced (63.4%)Imbalance
town_toa payoh is highly imbalanced (75.9%)Imbalance
town_woodlands is highly imbalanced (71.0%)Imbalance
town_yishun is highly imbalanced (68.7%)Imbalance
rent_approval_date has 2046 (3.4%) zerosZeros
coe_price_indicator has 2415 (4.1%) zerosZeros
stock_price has 1695 (2.8%) zerosZeros
town_importance has 3348 (5.6%) zerosZeros

Reproduction

Analysis started2023-11-07 05:59:26.073320
Analysis finished2023-11-07 06:00:55.492417
Duration1 minute and 29.42 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

rent_approval_date
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48723171
Minimum0
Maximum1
Zeros2046
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:55.639274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03402854
Q10.23271131
median0.46542261
Q30.76728869
95-th percentile0.96706915
Maximum1
Range1
Interquartile range (IQR)0.53457739

Descriptive statistics

Standard deviation0.30372927
Coefficient of variation (CV)0.62337747
Kurtosis-1.2426535
Mean0.48723171
Median Absolute Deviation (MAD)0.26673985
Skewness0.084859724
Sum28979.08
Variance0.092251468
MonotonicityNot monotonic
2023-11-07T06:00:55.906806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.06476399561 2415
 
4.1%
0.1317233809 2281
 
3.8%
0.165751921 2143
 
3.6%
0.4654226125 2126
 
3.6%
0.8660812294 2089
 
3.5%
0.4006586169 2082
 
3.5%
0.1986827662 2072
 
3.5%
0 2046
 
3.4%
0.2996706915 2036
 
3.4%
1 2025
 
3.4%
Other values (21) 38162
64.2%
ValueCountFrequency (%)
0 2046
3.4%
0.03402854007 1765
3.0%
0.06476399561 2415
4.1%
0.09879253568 1952
3.3%
0.1317233809 2281
3.8%
0.165751921 2143
3.6%
0.1986827662 2072
3.5%
0.2327113063 2015
3.4%
0.2667398463 1978
3.3%
0.2996706915 2036
3.4%
ValueCountFrequency (%)
1 2025
3.4%
0.9670691548 1923
3.2%
0.9330406147 1943
3.3%
0.9001097695 1986
3.3%
0.8660812294 2089
3.5%
0.8353457739 1927
3.2%
0.8013172338 1714
2.9%
0.7672886937 1736
2.9%
0.7343578485 1636
2.8%
0.7003293085 1695
2.8%

flat_type
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size464.8 KiB
0.5
21737 
0.25
18618 
0.75
14681 
1.0
3519 
0.0
 
922

Length

Max length4
Median length4
Mean length3.5598635
Min length3

Characters and Unicode

Total characters211730
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.25
2nd row0.5
3rd row0.25
4th row1.0
5th row0.25

Common Values

ValueCountFrequency (%)
0.5 21737
36.5%
0.25 18618
31.3%
0.75 14681
24.7%
1.0 3519
 
5.9%
0.0 922
 
1.6%

Length

2023-11-07T06:00:56.160770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T06:00:56.428878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.5 21737
36.5%
0.25 18618
31.3%
0.75 14681
24.7%
1.0 3519
 
5.9%
0.0 922
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 60399
28.5%
. 59477
28.1%
5 55036
26.0%
2 18618
 
8.8%
7 14681
 
6.9%
1 3519
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 152253
71.9%
Other Punctuation 59477
 
28.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60399
39.7%
5 55036
36.1%
2 18618
 
12.2%
7 14681
 
9.6%
1 3519
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 59477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 211730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60399
28.5%
. 59477
28.1%
5 55036
26.0%
2 18618
 
8.8%
7 14681
 
6.9%
1 3519
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 211730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60399
28.5%
. 59477
28.1%
5 55036
26.0%
2 18618
 
8.8%
7 14681
 
6.9%
1 3519
 
1.7%

floor_area_sqm
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33468831
Minimum0
Maximum1
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:56.683960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.14364641
Q10.21546961
median0.32596685
Q30.4198895
95-th percentile0.56353591
Maximum1
Range1
Interquartile range (IQR)0.20441989

Descriptive statistics

Standard deviation0.1330967
Coefficient of variation (CV)0.39767359
Kurtosis-0.53513167
Mean0.33468831
Median Absolute Deviation (MAD)0.10497238
Skewness0.2339137
Sum19906.257
Variance0.017714733
MonotonicityNot monotonic
2023-11-07T06:00:56.971500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.182320442 4755
 
8.0%
0.4198895028 3847
 
6.5%
0.1878453039 2526
 
4.2%
0.3867403315 2337
 
3.9%
0.320441989 2025
 
3.4%
0.3259668508 1852
 
3.1%
0.3093922652 1839
 
3.1%
0.3149171271 1767
 
3.0%
0.1712707182 1720
 
2.9%
0.4806629834 1664
 
2.8%
Other values (136) 35145
59.1%
ValueCountFrequency (%)
0 8
 
< 0.1%
0.01657458564 9
 
< 0.1%
0.02209944751 43
 
0.1%
0.03314917127 19
 
< 0.1%
0.04419889503 29
 
< 0.1%
0.04972375691 56
 
0.1%
0.05524861878 95
 
0.2%
0.06077348066 113
 
0.2%
0.06629834254 127
 
0.2%
0.07182320442 325
0.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.8729281768 5
 
< 0.1%
0.8563535912 12
< 0.1%
0.8397790055 4
 
< 0.1%
0.817679558 2
 
< 0.1%
0.8121546961 10
< 0.1%
0.8066298343 1
 
< 0.1%
0.8011049724 2
 
< 0.1%
0.7900552486 14
< 0.1%
0.7845303867 11
< 0.1%

lease_commence_date
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4701808
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:57.265069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13207547
Q10.28301887
median0.41509434
Q30.64150943
95-th percentile0.90566038
Maximum1
Range1
Interquartile range (IQR)0.35849057

Descriptive statistics

Standard deviation0.22883003
Coefficient of variation (CV)0.48668518
Kurtosis-0.70173667
Mean0.4701808
Median Absolute Deviation (MAD)0.16981132
Skewness0.29621931
Sum27964.943
Variance0.052363181
MonotonicityNot monotonic
2023-11-07T06:00:57.556146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.358490566 4127
 
6.9%
0.3396226415 3266
 
5.5%
0.2264150943 2587
 
4.3%
0.6603773585 2410
 
4.1%
0.4150943396 2385
 
4.0%
0.6981132075 2252
 
3.8%
0.3962264151 2004
 
3.4%
0.2452830189 1971
 
3.3%
0.5849056604 1915
 
3.2%
0.2641509434 1886
 
3.2%
Other values (44) 34674
58.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01886792453 513
0.9%
0.03773584906 87
 
0.1%
0.05660377358 424
 
0.7%
0.07547169811 834
1.4%
0.09433962264 440
0.7%
0.1132075472 401
 
0.7%
0.1320754717 566
1.0%
0.1509433962 620
1.0%
0.1698113208 1069
1.8%
ValueCountFrequency (%)
1 136
 
0.2%
0.9811320755 450
0.8%
0.9622641509 443
0.7%
0.9433962264 623
1.0%
0.9245283019 921
1.5%
0.9056603774 496
0.8%
0.8867924528 747
1.3%
0.8679245283 718
1.2%
0.8490566038 396
0.7%
0.8301886792 201
 
0.3%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct8612
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3595736
Minimum1.2703795
Maximum1.4570712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:57.839730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.2703795
5-th percentile1.2883811
Q11.3310669
median1.3542213
Q31.3870447
95-th percentile1.4384798
Maximum1.4570712
Range0.1866917
Interquartile range (IQR)0.055977792

Descriptive statistics

Standard deviation0.042468205
Coefficient of variation (CV)0.031236414
Kurtosis-0.42968472
Mean1.3595736
Median Absolute Deviation (MAD)0.027323797
Skewness0.24017567
Sum80863.361
Variance0.0018035484
MonotonicityNot monotonic
2023-11-07T06:00:58.130231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.309360395 56
 
0.1%
1.309053945 52
 
0.1%
1.320501696 44
 
0.1%
1.303787444 43
 
0.1%
1.330293034 43
 
0.1%
1.345057736 43
 
0.1%
1.340472872 41
 
0.1%
1.332242149 39
 
0.1%
1.334727136 38
 
0.1%
1.294801401 38
 
0.1%
Other values (8602) 59040
99.3%
ValueCountFrequency (%)
1.270379512 16
< 0.1%
1.270918709 19
< 0.1%
1.271408833 27
< 0.1%
1.271462566 8
 
< 0.1%
1.271690611 2
 
< 0.1%
1.271747245 4
 
< 0.1%
1.271757071 32
0.1%
1.27176738 13
< 0.1%
1.272044271 8
 
< 0.1%
1.272085585 14
< 0.1%
ValueCountFrequency (%)
1.457071216 15
< 0.1%
1.456546013 10
< 0.1%
1.456474318 4
 
< 0.1%
1.456424863 15
< 0.1%
1.456235091 3
 
< 0.1%
1.456112789 12
< 0.1%
1.456053408 12
< 0.1%
1.456027402 7
< 0.1%
1.45599869 3
 
< 0.1%
1.455952918 6
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct8612
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.8401
Minimum103.68523
Maximum103.96492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:58.442495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum103.68523
5-th percentile103.71515
Q1103.77862
median103.84538
Q3103.89751
95-th percentile103.94982
Maximum103.96492
Range0.27968701
Interquartile range (IQR)0.11889133

Descriptive statistics

Standard deviation0.071707028
Coefficient of variation (CV)0.00069055239
Kurtosis-0.9134692
Mean103.8401
Median Absolute Deviation (MAD)0.055112188
Skewness-0.23069241
Sum6176097.5
Variance0.0051418978
MonotonicityNot monotonic
2023-11-07T06:00:58.720753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.7932051 56
 
0.1%
103.7940631 52
 
0.1%
103.8633413 44
 
0.1%
103.8003853 43
 
0.1%
103.8591658 43
 
0.1%
103.7106998 43
 
0.1%
103.8461425 41
 
0.1%
103.8456431 39
 
0.1%
103.849823 38
 
0.1%
103.8544675 38
 
0.1%
Other values (8602) 59040
99.3%
ValueCountFrequency (%)
103.6852284 8
< 0.1%
103.6852343 1
 
< 0.1%
103.6859308 8
< 0.1%
103.6859367 8
< 0.1%
103.6860611 6
< 0.1%
103.6862886 11
< 0.1%
103.6863745 12
< 0.1%
103.6864521 5
< 0.1%
103.6866098 8
< 0.1%
103.6868369 5
< 0.1%
ValueCountFrequency (%)
103.9649154 2
 
< 0.1%
103.9647218 5
< 0.1%
103.9643986 3
< 0.1%
103.9642038 5
< 0.1%
103.9641132 4
< 0.1%
103.9639632 1
 
< 0.1%
103.9638826 3
< 0.1%
103.9638194 3
< 0.1%
103.9637505 3
< 0.1%
103.963727 5
< 0.1%

monthly_rent
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2591.5127
Minimum300
Maximum6950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:59.010545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile1700
Q12100
median2400
Q33050
95-th percentile4000
Maximum6950
Range6650
Interquartile range (IQR)950

Descriptive statistics

Standard deviation715.199
Coefficient of variation (CV)0.27597743
Kurtosis0.81281886
Mean2591.5127
Median Absolute Deviation (MAD)450
Skewness0.86020229
Sum1.541354 × 108
Variance511509.61
MonotonicityNot monotonic
2023-11-07T06:00:59.296524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 5076
 
8.5%
2600 3532
 
5.9%
2300 3525
 
5.9%
1900 3304
 
5.6%
2400 2980
 
5.0%
2200 2858
 
4.8%
3150 2839
 
4.8%
2500 2806
 
4.7%
2950 2412
 
4.1%
2000 2407
 
4.0%
Other values (103) 27738
46.6%
ValueCountFrequency (%)
300 1
 
< 0.1%
400 5
 
< 0.1%
450 2
 
< 0.1%
500 11
 
< 0.1%
650 17
< 0.1%
700 1
 
< 0.1%
750 13
 
< 0.1%
800 12
 
< 0.1%
850 35
0.1%
900 5
 
< 0.1%
ValueCountFrequency (%)
6950 1
 
< 0.1%
6500 3
 
< 0.1%
6450 1
 
< 0.1%
6400 1
 
< 0.1%
6300 12
< 0.1%
6200 2
 
< 0.1%
6100 3
 
< 0.1%
6000 3
 
< 0.1%
5900 2
 
< 0.1%
5800 7
< 0.1%

distance_to_nearest_existing_mrt
Real number (ℝ)

HIGH CORRELATION 

Distinct8612
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27623828
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:00:59.587085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.069162697
Q10.15508833
median0.24389733
Q30.3550801
95-th percentile0.63560245
Maximum1
Range1
Interquartile range (IQR)0.19999178

Descriptive statistics

Standard deviation0.16869085
Coefficient of variation (CV)0.61067153
Kurtosis1.5056288
Mean0.27623828
Median Absolute Deviation (MAD)0.09773034
Skewness1.1824314
Sum16429.824
Variance0.028456604
MonotonicityNot monotonic
2023-11-07T06:00:59.886250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1764350672 56
 
0.1%
0.1681470586 52
 
0.1%
0.06222129278 44
 
0.1%
0.4109542395 43
 
0.1%
0.452720065 43
 
0.1%
0.3474904058 43
 
0.1%
0.01778547138 41
 
0.1%
0.0748310835 39
 
0.1%
0.1263190178 38
 
0.1%
0.07819861726 38
 
0.1%
Other values (8602) 59040
99.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.00309482685 1
 
< 0.1%
0.005210424239 9
< 0.1%
0.006044531885 5
 
< 0.1%
0.007374612226 17
< 0.1%
0.007651031452 10
< 0.1%
0.009424497684 12
< 0.1%
0.009969859288 12
< 0.1%
0.01078261702 10
< 0.1%
0.0109997424 9
< 0.1%
ValueCountFrequency (%)
1 9
< 0.1%
0.9984809337 2
 
< 0.1%
0.9958525934 6
< 0.1%
0.99434413 8
< 0.1%
0.9917912155 9
< 0.1%
0.9880542656 3
 
< 0.1%
0.9742745488 3
 
< 0.1%
0.9704374721 2
 
< 0.1%
0.9649275123 8
< 0.1%
0.9569431943 2
 
< 0.1%

distance_to_nearest_planned_mrt
Real number (ℝ)

HIGH CORRELATION 

Distinct21194
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21981207
Minimum0
Maximum1
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:00.820050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.029556025
Q10.075149913
median0.14343272
Q30.27621909
95-th percentile0.72357333
Maximum1
Range1
Interquartile range (IQR)0.20106918

Descriptive statistics

Standard deviation0.21334755
Coefficient of variation (CV)0.97059071
Kurtosis2.2677201
Mean0.21981207
Median Absolute Deviation (MAD)0.082502822
Skewness1.6868635
Sum13073.762
Variance0.045517178
MonotonicityNot monotonic
2023-11-07T06:01:01.145232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3017745676 25
 
< 0.1%
0.2923963832 24
 
< 0.1%
0.05251718585 19
 
< 0.1%
0.2963877934 18
 
< 0.1%
0.3914362976 18
 
< 0.1%
0.4223131622 18
 
< 0.1%
0.05536902511 18
 
< 0.1%
0.04010044162 17
 
< 0.1%
0.3982235501 17
 
< 0.1%
0.09536725196 17
 
< 0.1%
Other values (21184) 59286
99.7%
ValueCountFrequency (%)
0 3
< 0.1%
4.531662564 × 10-53
< 0.1%
0.0001684999856 3
< 0.1%
0.0003611277893 1
 
< 0.1%
0.0003777988442 1
 
< 0.1%
0.0004231154698 2
 
< 0.1%
0.0009069313814 2
 
< 0.1%
0.000952248007 5
< 0.1%
0.001075431367 3
< 0.1%
0.001703100963 1
 
< 0.1%
ValueCountFrequency (%)
1 4
< 0.1%
0.9999938671 4
< 0.1%
0.9999916109 3
< 0.1%
0.9957070364 2
 
< 0.1%
0.9957009035 3
< 0.1%
0.9956986473 5
< 0.1%
0.9950152334 1
 
< 0.1%
0.9950091005 4
< 0.1%
0.9950068443 1
 
< 0.1%
0.9906999551 2
 
< 0.1%
Distinct8612
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18826401
Minimum0
Maximum1
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:01.457864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04380659
Q10.10071345
median0.16704612
Q30.24389124
95-th percentile0.40541743
Maximum1
Range1
Interquartile range (IQR)0.14317779

Descriptive statistics

Standard deviation0.12233569
Coefficient of variation (CV)0.64980923
Kurtosis4.5971131
Mean0.18826401
Median Absolute Deviation (MAD)0.070180424
Skewness1.6496777
Sum11197.379
Variance0.014966021
MonotonicityNot monotonic
2023-11-07T06:01:01.759633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6009077972 56
 
0.1%
0.589286893 52
 
0.1%
0.1123726681 44
 
0.1%
0.2057836789 43
 
0.1%
0.3185870443 43
 
0.1%
0.1532218243 43
 
0.1%
0.158675469 41
 
0.1%
0.1879774216 39
 
0.1%
0.1829692625 38
 
0.1%
0.2770030468 38
 
0.1%
Other values (8602) 59040
99.3%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.0002728986156 5
< 0.1%
0.0009686685211 1
 
< 0.1%
0.001543464716 8
< 0.1%
0.003099846512 7
< 0.1%
0.003271392157 3
 
< 0.1%
0.003387150522 4
< 0.1%
0.003546206326 5
< 0.1%
0.004169222923 8
< 0.1%
0.004394531156 5
< 0.1%
ValueCountFrequency (%)
1 9
< 0.1%
0.9788216658 9
< 0.1%
0.9092373967 2
 
< 0.1%
0.8963927562 4
 
< 0.1%
0.8894076141 2
 
< 0.1%
0.8807434461 11
< 0.1%
0.8778310111 7
< 0.1%
0.8766295351 16
< 0.1%
0.8722437393 11
< 0.1%
0.8672753087 12
< 0.1%

distance_to_nearest_mall
Real number (ℝ)

Distinct8610
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28840813
Minimum0
Maximum1
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:02.058497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07967214
Q10.16290685
median0.25573291
Q30.38321626
95-th percentile0.60667077
Maximum1
Range1
Interquartile range (IQR)0.2203094

Descriptive statistics

Standard deviation0.16444361
Coefficient of variation (CV)0.57017673
Kurtosis0.88648692
Mean0.28840813
Median Absolute Deviation (MAD)0.10532663
Skewness0.9686162
Sum17153.65
Variance0.027041699
MonotonicityNot monotonic
2023-11-07T06:01:02.363762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1082880121 56
 
0.1%
0.08066057501 52
 
0.1%
0.4606081521 44
 
0.1%
0.4519497509 43
 
0.1%
0.3893175891 43
 
0.1%
0.1053433025 43
 
0.1%
0.4406767292 41
 
0.1%
0.2009600243 39
 
0.1%
0.1294752796 38
 
0.1%
0.08674479678 38
 
0.1%
Other values (8600) 59040
99.3%
ValueCountFrequency (%)
0 24
< 0.1%
0.0168879638 4
 
< 0.1%
0.01798326118 1
 
< 0.1%
0.02064270963 3
 
< 0.1%
0.02225947747 6
 
< 0.1%
0.0223438589 8
 
< 0.1%
0.02396599428 8
 
< 0.1%
0.02478933152 3
 
< 0.1%
0.02480085103 1
 
< 0.1%
0.02631180021 5
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.9878568586 3
 
< 0.1%
0.9810172682 1
 
< 0.1%
0.9780806901 2
 
< 0.1%
0.9758512926 8
< 0.1%
0.9703874336 9
< 0.1%
0.9690287648 1
 
< 0.1%
0.964168854 3
 
< 0.1%
0.9606800058 5
< 0.1%
0.9593876162 5
< 0.1%

coe_price_indicator
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44589397
Minimum0
Maximum1
Zeros2415
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:02.650764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.010738533
Q10.17757005
median0.44297155
Q30.71163665
95-th percentile0.91814793
Maximum1
Range1
Interquartile range (IQR)0.5340666

Descriptive statistics

Standard deviation0.30042704
Coefficient of variation (CV)0.67376341
Kurtosis-1.2597543
Mean0.44589397
Median Absolute Deviation (MAD)0.2686651
Skewness0.1505449
Sum26520.435
Variance0.090256405
MonotonicityNot monotonic
2023-11-07T06:01:02.939622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 2415
 
4.1%
0.1136137932 2281
 
3.8%
0.127925987 2143
 
3.6%
0.5712748905 2126
 
3.6%
0.8182288436 2089
 
3.5%
0.2574032791 2082
 
3.5%
0.1138837114 2072
 
3.5%
0.03136612426 2046
 
3.4%
0.3155434575 2036
 
3.4%
0.7489064418 2025
 
3.4%
Other values (21) 38162
64.2%
ValueCountFrequency (%)
0 2415
4.1%
0.01073853315 1765
3.0%
0.03136612426 2046
3.4%
0.1136137932 2281
3.8%
0.1138837114 2072
3.5%
0.127925987 2143
3.6%
0.1383799114 2015
3.4%
0.1775700466 1978
3.3%
0.1839795927 1952
3.3%
0.2574032791 2082
3.5%
ValueCountFrequency (%)
1 1923
3.2%
0.9181479301 1927
3.2%
0.9014622739 1943
3.3%
0.8482776173 1986
3.3%
0.8182288436 2089
3.5%
0.7502918499 1636
2.8%
0.7489064418 2025
3.4%
0.7116366457 1714
2.9%
0.6896173813 1736
2.9%
0.6623415293 1695
2.8%

stock_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3593495
Minimum0
Maximum1
Zeros1695
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:03.311486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0002188901
Q10.089978377
median0.23769509
Q30.65037644
95-th percentile0.94077644
Maximum1
Range1
Interquartile range (IQR)0.56039806

Descriptive statistics

Standard deviation0.30867603
Coefficient of variation (CV)0.85898555
Kurtosis-1.0740737
Mean0.3593495
Median Absolute Deviation (MAD)0.20634906
Skewness0.52480362
Sum21373.03
Variance0.095280891
MonotonicityNot monotonic
2023-11-07T06:01:03.725419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.6780638318 2415
 
4.1%
0.4695996129 2281
 
3.8%
0.7512745854 2143
 
3.6%
0.4058696966 2126
 
3.6%
0.185665965 2089
 
3.5%
0.4010434145 2082
 
3.5%
0.6503764375 2072
 
3.5%
0.342892115 2046
 
3.4%
1 2036
 
3.4%
0.0002188900994 2025
 
3.4%
Other values (21) 38162
64.2%
ValueCountFrequency (%)
0 1695
2.8%
0.0002188900994 2025
3.4%
0.007424626632 1986
3.3%
0.0205851391 1714
2.9%
0.03134602254 1717
2.9%
0.05368591934 1927
3.2%
0.0579336022 1923
3.2%
0.07317856868 1736
2.9%
0.08997837682 1636
2.8%
0.1073461205 1825
3.1%
ValueCountFrequency (%)
1 2036
3.4%
0.9407764381 1892
3.2%
0.7964433293 1978
3.3%
0.7949252457 2015
3.4%
0.7512745854 2143
3.6%
0.6833766305 2018
3.4%
0.6780638318 2415
4.1%
0.6503764375 2072
3.5%
0.6193154845 1952
3.3%
0.4695996129 2281
3.8%

town_importance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15477613
Minimum0
Maximum1
Zeros3348
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:04.193392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.091549296
median0.13380282
Q30.18309859
95-th percentile0.29577465
Maximum1
Range1
Interquartile range (IQR)0.091549296

Descriptive statistics

Standard deviation0.13612234
Coefficient of variation (CV)0.87947889
Kurtosis14.609031
Mean0.15477613
Median Absolute Deviation (MAD)0.042253521
Skewness3.1439396
Sum9205.6197
Variance0.018529291
MonotonicityNot monotonic
2023-11-07T06:01:04.626810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.1056338028 4959
 
8.3%
0.0985915493 4729
 
8.0%
0.1338028169 4389
 
7.4%
0.2253521127 4161
 
7.0%
0.1478873239 3974
 
6.7%
0.1338028169 3607
 
6.1%
0.04929577465 3460
 
5.8%
0 3348
 
5.6%
0.2957746479 3110
 
5.2%
0.09154929577 2684
 
4.5%
Other values (13) 21056
35.4%
ValueCountFrequency (%)
0 3348
5.6%
0.01408450704 1433
 
2.4%
0.04225352113 1698
 
2.9%
0.04929577465 3460
5.8%
0.07042253521 1658
 
2.8%
0.08450704225 2000
3.4%
0.09154929577 2684
4.5%
0.0985915493 4729
8.0%
0.1056338028 4959
8.3%
0.1197183099 2221
3.7%
ValueCountFrequency (%)
1 600
 
1.0%
0.5563380282 1833
3.1%
0.2957746479 3110
5.2%
0.2816901408 2360
4.0%
0.2676056338 1699
2.9%
0.2253521127 4161
7.0%
0.1830985915 1428
 
2.4%
0.161971831 1287
 
2.2%
0.1549295775 2238
3.8%
0.1478873239 3974
6.7%

town_centrality_page_rank
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038431447
Minimum0.038137875
Maximum0.038819414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:05.083158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.038137875
5-th percentile0.038137875
Q10.038302079
median0.038403003
Q30.038601702
95-th percentile0.038798945
Maximum0.038819414
Range0.00068153869
Interquartile range (IQR)0.00029962361

Descriptive statistics

Standard deviation0.00018912345
Coefficient of variation (CV)0.0049210598
Kurtosis-0.71948962
Mean0.038431447
Median Absolute Deviation (MAD)0.00014239685
Skewness0.30253899
Sum2285.7872
Variance3.5767678 × 10-8
MonotonicityNot monotonic
2023-11-07T06:01:05.543064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.03813787514 4389
 
7.4%
0.03830207874 4161
 
7.0%
0.03847978101 3974
 
6.7%
0.0384138753 3607
 
6.1%
0.03855808124 3460
 
5.8%
0.03833745924 3348
 
5.6%
0.03860170235 3110
 
5.2%
0.03816899271 3022
 
5.1%
0.03862117861 2848
 
4.8%
0.03832692835 2684
 
4.5%
Other values (16) 24874
41.8%
ValueCountFrequency (%)
0.03813787514 4389
7.4%
0.03816899271 3022
5.1%
0.03817291631 1433
 
2.4%
0.03823511594 2111
3.5%
0.03829801499 1707
 
2.9%
0.03830207874 4161
7.0%
0.03832333448 1698
 
2.9%
0.03832692835 2684
4.5%
0.03833745924 3348
5.6%
0.03838669017 2221
3.7%
ValueCountFrequency (%)
0.03881941383 2360
4.0%
0.03879894459 1833
3.1%
0.03872875822 1699
2.9%
0.03865650944 1287
 
2.2%
0.03864102544 1428
2.4%
0.03863009006 601
 
1.0%
0.03862117861 2848
4.8%
0.03860170235 3110
5.2%
0.03855808124 3460
5.8%
0.03854539967 600
 
1.0%

flat_type_model
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51495226
Minimum0
Maximum1
Zeros465
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size464.8 KiB
2023-11-07T06:01:06.032046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21052632
Q10.34210526
median0.55263158
Q30.73684211
95-th percentile0.92105263
Maximum1
Range1
Interquartile range (IQR)0.39473684

Descriptive statistics

Standard deviation0.21675427
Coefficient of variation (CV)0.42092109
Kurtosis-0.7842653
Mean0.51495226
Median Absolute Deviation (MAD)0.18421053
Skewness-0.050718594
Sum30627.816
Variance0.046982413
MonotonicityNot monotonic
2023-11-07T06:01:06.536129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.5526315789 12283
20.7%
0.7368421053 10736
18.1%
0.3421052632 7735
13.0%
0.2105263158 4927
8.3%
0.2631578947 3474
 
5.8%
0.6052631579 3433
 
5.8%
0.5263157895 2249
 
3.8%
0.6578947368 2232
 
3.8%
0.9210526316 2077
 
3.5%
0.4473684211 1821
 
3.1%
Other values (29) 8510
14.3%
ValueCountFrequency (%)
0 465
 
0.8%
0.02631578947 221
 
0.4%
0.05263157895 34
 
0.1%
0.07894736842 194
 
0.3%
0.1052631579 8
 
< 0.1%
0.1315789474 68
 
0.1%
0.1578947368 1350
 
2.3%
0.1842105263 109
 
0.2%
0.2105263158 4927
8.3%
0.2368421053 952
 
1.6%
ValueCountFrequency (%)
1 6
 
< 0.1%
0.9736842105 6
 
< 0.1%
0.9473684211 953
1.6%
0.9210526316 2077
3.5%
0.8947368421 803
 
1.4%
0.8684210526 56
 
0.1%
0.8421052632 477
 
0.8%
0.8157894737 35
 
0.1%
0.7894736842 1
 
< 0.1%
0.7631578947 8
 
< 0.1%

region_central region
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
45790 
True
13687 
ValueCountFrequency (%)
False 45790
77.0%
True 13687
 
23.0%
2023-11-07T06:01:07.034818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

region_east region
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
50002 
True
9475 
ValueCountFrequency (%)
False 50002
84.1%
True 9475
 
15.9%
2023-11-07T06:01:07.437207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

region_north region
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
51674 
True
7803 
ValueCountFrequency (%)
False 51674
86.9%
True 7803
 
13.1%
2023-11-07T06:01:07.885120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

region_north-east region
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
45224 
True
14253 
ValueCountFrequency (%)
False 45224
76.0%
True 14253
 
24.0%
2023-11-07T06:01:08.188236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

region_west region
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
45218 
True
14259 
ValueCountFrequency (%)
False 45218
76.0%
True 14259
 
24.0%
2023-11-07T06:01:08.392741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_ang mo kio
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
56017 
True
 
3460
ValueCountFrequency (%)
False 56017
94.2%
True 3460
 
5.8%
2023-11-07T06:01:08.591706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_bedok
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
55870 
True
 
3607
ValueCountFrequency (%)
False 55870
93.9%
True 3607
 
6.1%
2023-11-07T06:01:08.808790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_bishan
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
58049 
True
 
1428
ValueCountFrequency (%)
False 58049
97.6%
True 1428
 
2.4%
2023-11-07T06:01:09.008938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_bukit batok
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57256 
True
 
2221
ValueCountFrequency (%)
False 57256
96.3%
True 2221
 
3.7%
2023-11-07T06:01:09.205814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_bukit merah
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
56367 
True
 
3110
ValueCountFrequency (%)
False 56367
94.8%
True 3110
 
5.2%
2023-11-07T06:01:09.402146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_bukit panjang
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57875 
True
 
1602
ValueCountFrequency (%)
False 57875
97.3%
True 1602
 
2.7%
2023-11-07T06:01:09.603697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_bukit timah
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
59421 
True
 
56
ValueCountFrequency (%)
False 59421
99.9%
True 56
 
0.1%
2023-11-07T06:01:09.801637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_central
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
58877 
True
 
600
ValueCountFrequency (%)
False 58877
99.0%
True 600
 
1.0%
2023-11-07T06:01:10.011426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_choa chu kang
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57366 
True
 
2111
ValueCountFrequency (%)
False 57366
96.5%
True 2111
 
3.5%
2023-11-07T06:01:10.222222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_clementi
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57239 
True
 
2238
ValueCountFrequency (%)
False 57239
96.2%
True 2238
 
3.8%
2023-11-07T06:01:10.445567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_geylang
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57778 
True
 
1699
ValueCountFrequency (%)
False 57778
97.1%
True 1699
 
2.9%
2023-11-07T06:01:10.647415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_hougang
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
56629 
True
 
2848
ValueCountFrequency (%)
False 56629
95.2%
True 2848
 
4.8%
2023-11-07T06:01:10.883703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_jurong east
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57779 
True
 
1698
ValueCountFrequency (%)
False 57779
97.1%
True 1698
 
2.9%
2023-11-07T06:01:11.088634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_jurong west
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
55088 
True
 
4389
ValueCountFrequency (%)
False 55088
92.6%
True 4389
 
7.4%
2023-11-07T06:01:11.294945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_kallang/whampoa
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57644 
True
 
1833
ValueCountFrequency (%)
False 57644
96.9%
True 1833
 
3.1%
2023-11-07T06:01:11.492815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_marine parade
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
58876 
True
 
601
ValueCountFrequency (%)
False 58876
99.0%
True 601
 
1.0%
2023-11-07T06:01:11.688463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_pasir ris
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57770 
True
 
1707
ValueCountFrequency (%)
False 57770
97.1%
True 1707
 
2.9%
2023-11-07T06:01:11.915624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_punggol
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
56793 
True
 
2684
ValueCountFrequency (%)
False 56793
95.5%
True 2684
 
4.5%
2023-11-07T06:01:12.115853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_queenstown
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57477 
True
 
2000
ValueCountFrequency (%)
False 57477
96.6%
True 2000
 
3.4%
2023-11-07T06:01:12.310429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_sembawang
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
58044 
True
 
1433
ValueCountFrequency (%)
False 58044
97.6%
True 1433
 
2.4%
2023-11-07T06:01:12.505742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_sengkang
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
55503 
True
 
3974
ValueCountFrequency (%)
False 55503
93.3%
True 3974
 
6.7%
2023-11-07T06:01:12.708354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_serangoon
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
58190 
True
 
1287
ValueCountFrequency (%)
False 58190
97.8%
True 1287
 
2.2%
2023-11-07T06:01:12.910172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_tampines
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
55316 
True
 
4161
ValueCountFrequency (%)
False 55316
93.0%
True 4161
 
7.0%
2023-11-07T06:01:13.116446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_toa payoh
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
57117 
True
 
2360
ValueCountFrequency (%)
False 57117
96.0%
True 2360
 
4.0%
2023-11-07T06:01:13.316923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_woodlands
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
56455 
True
 
3022
ValueCountFrequency (%)
False 56455
94.9%
True 3022
 
5.1%
2023-11-07T06:01:13.511138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

town_yishun
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
False
56129 
True
 
3348
ValueCountFrequency (%)
False 56129
94.4%
True 3348
 
5.6%
2023-11-07T06:01:13.703152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-11-07T06:00:47.336901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:43.541819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:47.547512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:52.919379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:57.776510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:01.786439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:07.494632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:11.879757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:15.806782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:20.889112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:25.715551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:29.537399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:33.869202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:39.079592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:43.406272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:47.586782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:43.789109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:47.942707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:53.185758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:58.039038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:02.060334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:07.757773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:12.152324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:16.063812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:21.308702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:25.963996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:29.783196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:34.268469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:39.329192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:43.655915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:47.858818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:44.032061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:48.326823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:53.424513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:58.297459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:02.465577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:08.357822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:12.402508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:16.313870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:21.716026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:26.224081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:30.028069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:34.653672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:39.565774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:43.921916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:48.252316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:44.274806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:48.708714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:53.666930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:58.553982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:02.848698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:08.621657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:12.651423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:16.561093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:22.144979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:26.469550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:30.297527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:35.035534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:39.824658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:44.181572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:48.655402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:44.531418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:49.127749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:53.925920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:58.818676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:03.267185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:08.890368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:12.926670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:16.828726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:22.553250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:26.733333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:30.579922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:35.452143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:40.086697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:44.449325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:49.055607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:44.780592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:49.513296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:54.190858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:59.081963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:03.614614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:09.156195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:13.185535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:17.092854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:22.804908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:26.990960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:30.850506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:35.852466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:40.867825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:44.705750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:49.461395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:45.042783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:49.952981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:54.447243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:59.355042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:04.034560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:09.413632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:13.444383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:17.350091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:23.449643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:27.270298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:31.136895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:36.210219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:41.137764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:44.980421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:49.853162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:45.285772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:50.293542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:54.695506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:59.610170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:04.442317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:09.674241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:13.701032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:17.659822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:23.692112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:27.522441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:31.407473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:36.605481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:41.384455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:45.237963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:50.243909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:45.524219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:50.706047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:54.960520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:59.874908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:04.847144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:09.944323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:13.973732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:18.032331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:23.950162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:27.774265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:31.662821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:37.009203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:41.631998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:45.497873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:50.649982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:45.774840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:51.119065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:56.275505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:00.140445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:05.265791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:10.220612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:14.230371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:18.431398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:24.209385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:28.029672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:31.906716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:37.439380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:41.892167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:45.761888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:51.045502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:46.029362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:51.511361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:56.514859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:00.425462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:05.628262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:10.492174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:14.493584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:18.837096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:24.459997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:28.289233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:32.149888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:37.810649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:42.136875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:46.034421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:51.440242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:46.265950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:51.901139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:56.771396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:00.699179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:06.026426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:10.786478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:14.745059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:19.231963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:24.700826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:28.534120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:32.387934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:38.066555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:42.376919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:46.290359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:51.836100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:46.499874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:52.186319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:57.019314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:00.976722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:06.364357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:11.067949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:15.016856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:19.625541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:24.951088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:28.777315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:32.710367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:38.319073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:42.621385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:46.544882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:52.228483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:46.753875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:52.419786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:57.265546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:01.249947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:06.765890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:11.329273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:15.276712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:20.038710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:25.209298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:29.026464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:33.108350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:38.560429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:42.885953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:46.800040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:52.663280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:47.158808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:52.673542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T05:59:57.528028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:01.528440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:07.155776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:11.604282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:15.539513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:20.477657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:25.469189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:29.286714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:33.516374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:38.830846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:43.155439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-07T06:00:47.078149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-07T06:01:13.990501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
rent_approval_datefloor_area_sqmlease_commence_datelatitudelongitudemonthly_rentdistance_to_nearest_existing_mrtdistance_to_nearest_planned_mrtdistance_to_nearest_schooldistance_to_nearest_mallcoe_price_indicatorstock_pricetown_importancetown_centrality_page_rankflat_type_modelflat_typeregion_central regionregion_east regionregion_north regionregion_north-east regionregion_west regiontown_ang mo kiotown_bedoktown_bishantown_bukit batoktown_bukit merahtown_bukit panjangtown_bukit timahtown_centraltown_choa chu kangtown_clementitown_geylangtown_hougangtown_jurong easttown_jurong westtown_kallang/whampoatown_marine paradetown_pasir ristown_punggoltown_queenstowntown_sembawangtown_sengkangtown_serangoontown_tampinestown_toa payohtown_woodlandstown_yishun
rent_approval_date1.000-0.0190.018-0.0050.0000.513-0.005-0.0020.0030.0000.967-0.7890.0040.006-0.0180.0150.0080.0000.0060.0100.0000.0000.0000.0000.0060.0150.0050.0000.0000.0170.0120.0000.0030.0060.0100.0000.0000.0150.0190.0070.0100.0070.0070.0000.0070.0000.006
floor_area_sqm-0.0191.0000.4490.3010.0010.3190.082-0.049-0.119-0.162-0.0160.016-0.122-0.3260.9400.9030.2630.1500.1380.1270.0920.1500.1130.0300.0540.0850.0860.0180.0720.1380.1330.1270.0480.0360.1240.1100.1170.2410.1630.1270.1270.1900.0470.1350.1420.1640.092
lease_commence_date0.0180.4491.0000.437-0.0240.2230.0970.021-0.181-0.2560.018-0.020-0.176-0.3370.4530.3740.5630.3380.2260.3240.1860.4640.3120.2230.2220.2450.1860.0950.1290.2260.3510.2320.1590.1480.1650.2160.3670.3440.3560.3300.2610.3820.1870.2990.3280.2460.298
latitude-0.0050.3010.4371.0000.050-0.0970.1280.202-0.274-0.254-0.0030.003-0.608-0.4260.2950.1920.7030.3830.9660.7080.3180.5210.4250.3150.3430.9130.2690.0670.2640.2980.4690.3490.3890.2350.4040.3960.4120.3760.5440.5850.6840.6970.2140.5210.4540.6350.735
longitude0.0000.001-0.0240.0501.000-0.0010.048-0.024-0.1040.0890.0030.0000.2690.310-0.0190.1380.4880.9140.5650.6710.9630.4020.5750.2820.5090.4400.3480.1150.1610.4340.4340.3780.4400.4720.9020.4380.2730.4520.4000.4390.4500.5170.4260.6830.3160.6060.526
monthly_rent0.5130.3190.223-0.097-0.0011.000-0.091-0.0350.004-0.0810.501-0.4450.0990.0240.3220.2160.1400.0410.0860.0590.0220.0580.0520.0660.0390.1210.0270.0160.1280.0330.0270.0380.0250.0020.0360.0220.0140.0360.0520.0910.0270.0480.0290.0380.0230.0440.075
distance_to_nearest_existing_mrt-0.0050.0820.0970.1280.048-0.0911.000-0.197-0.0000.153-0.0030.002-0.162-0.0900.0600.0770.2180.1070.0910.2300.1490.1140.1460.0610.0590.1000.1220.0460.1520.0570.0780.1700.1270.1010.0800.1350.5100.1900.2320.0750.0850.2510.2030.1070.1230.0710.050
distance_to_nearest_planned_mrt-0.002-0.0490.0210.202-0.024-0.035-0.1971.000-0.0490.0060.0000.0020.0030.096-0.0710.1150.6000.2350.9540.3160.3540.2060.1440.1280.1760.2000.2710.0500.1050.1650.2250.2460.1620.2240.2400.2860.1330.2200.2390.4090.9710.3030.0950.2850.5010.5870.756
distance_to_nearest_school0.003-0.119-0.181-0.274-0.1040.004-0.000-0.0491.0000.0710.0040.0010.1080.121-0.1130.0710.1840.1120.1360.1270.1270.1120.1030.1090.0550.0580.0590.0360.1790.0640.1780.1030.0720.3470.0850.2130.0640.0830.1470.2090.1160.1010.0930.0590.0600.0670.094
distance_to_nearest_mall0.000-0.162-0.256-0.2540.089-0.0810.1530.0060.0711.000-0.001-0.0020.1090.209-0.1500.1040.2220.2130.1430.1700.1360.1700.3710.0670.1740.2160.1040.0460.1260.1100.1220.2280.1120.2020.1020.2000.0380.0790.1260.0680.0740.1720.0730.0720.0950.1600.094
coe_price_indicator0.967-0.0160.018-0.0030.0030.501-0.0030.0000.004-0.0011.000-0.7480.0050.007-0.0160.0130.0000.0000.0110.0040.0030.0000.0100.0000.0000.0090.0000.0000.0110.0080.0110.0000.0080.0000.0000.0000.0000.0100.0160.0100.0100.0000.0000.0000.0000.0050.010
stock_price-0.7890.016-0.0200.0030.000-0.4450.0020.0020.001-0.002-0.7481.000-0.004-0.0040.0150.0110.0050.0000.0030.0000.0000.0000.0060.0000.0030.0140.0040.0080.0090.0000.0000.0000.0000.0000.0000.0000.0000.0070.0090.0050.0060.0030.0000.0000.0090.0000.004
town_importance0.004-0.122-0.176-0.6080.2690.099-0.1620.0030.1080.1090.005-0.0041.0000.430-0.1510.0850.6300.2930.5260.3100.4250.3360.3010.1860.2340.4840.2250.0411.0000.2270.2340.3530.2660.2320.3351.0000.1200.2320.2940.2520.2120.3170.1760.5650.4190.3130.330
town_centrality_page_rank0.006-0.326-0.337-0.4260.3100.024-0.0900.0960.1210.2090.007-0.0040.4301.000-0.3000.1880.7510.7620.5490.6140.8120.7051.0000.4610.5850.6660.4940.0860.2851.0000.5871.0000.6590.3150.6750.6470.2970.3160.3990.5270.3760.7550.4370.5040.7380.5530.448
flat_type_model-0.0180.9400.4530.295-0.0190.3220.060-0.071-0.113-0.150-0.0160.015-0.151-0.3001.0000.9480.3870.1090.1650.1850.1250.3590.1960.0990.0660.1420.0820.0240.1060.1450.2520.2570.0620.0700.1280.1350.1380.2000.1620.2470.1230.1780.0800.1260.2010.1150.146
flat_type0.0150.9030.3740.1920.1380.2160.0770.1150.0710.1040.0130.0110.0850.1880.9481.0000.2190.0600.1060.0750.0640.1820.1020.0520.0530.0840.0690.0140.0440.1140.1300.0990.0440.0230.1140.0900.0620.1660.1430.0990.1230.1730.0400.0350.1210.1100.054
region_central region0.0080.2630.5630.7030.4880.1400.2180.6000.1840.2220.0000.0050.6300.7510.3870.2191.0000.2380.2120.3070.3070.1360.1390.2870.1070.4300.0910.0550.1840.1050.1080.3140.1220.0940.1540.3260.1850.0940.1190.3410.0860.1460.0810.1500.3720.1260.133
region_east region0.0000.1500.3380.3830.9140.0410.1070.2350.1120.2130.0000.0000.2930.7620.1090.0600.2381.0000.1690.2440.2440.1080.5840.0680.0860.1020.0720.0120.0440.0830.0860.0740.0970.0740.1230.0770.0440.3950.0940.0810.0680.1160.0640.6300.0880.1010.106
region_north region0.0060.1380.2260.9660.5650.0860.0910.9540.1360.1430.0110.0030.5260.5490.1650.1060.2120.1691.0000.2180.2180.0960.0990.0610.0760.0910.0640.0100.0390.0740.0770.0660.0870.0660.1100.0690.0390.0670.0840.0720.4040.1040.0570.1060.0790.5950.628
region_north-east region0.0100.1270.3240.7080.6710.0590.2300.3160.1270.1700.0040.0000.3100.6140.1850.0750.3070.2440.2181.0000.3150.4430.1430.0880.1100.1320.0930.0160.0560.1080.1110.0960.3990.0960.1580.1000.0560.0960.3870.1050.0880.4770.2650.1540.1140.1300.137
region_west region0.0000.0920.1860.3180.9630.0220.1490.3540.1270.1360.0030.0000.4250.8120.1250.0640.3070.2440.2180.3151.0000.1390.1430.0880.3510.1320.2960.0160.0560.3410.3520.0960.1260.3050.5030.1000.0560.0960.1220.1050.0880.1500.0830.1540.1140.1300.137
town_ang mo kio0.0000.1500.4640.5210.4020.0580.1140.2060.1120.1700.0000.0000.3360.7050.3590.1820.1360.1080.0960.4430.1391.0000.0630.0390.0490.0580.0410.0050.0240.0470.0490.0420.0550.0420.0700.0440.0240.0420.0540.0460.0390.0660.0360.0680.0500.0570.060
town_bedok0.0000.1130.3120.4250.5750.0520.1460.1440.1030.3710.0100.0060.3011.0000.1960.1020.1390.5840.0990.1430.1430.0631.0000.0390.0500.0590.0420.0050.0250.0480.0500.0430.0570.0430.0710.0450.0250.0430.0550.0470.0390.0680.0370.0690.0510.0580.062
town_bishan0.0000.0300.2230.3150.2820.0660.0610.1280.1090.0670.0000.0000.1860.4610.0990.0520.2870.0680.0610.0880.0880.0390.0391.0000.0300.0360.0250.0000.0150.0300.0300.0260.0350.0260.0440.0270.0150.0260.0340.0290.0240.0420.0230.0430.0310.0360.038
town_bukit batok0.0060.0540.2220.3430.5090.0390.0590.1760.0550.1740.0000.0030.2340.5850.0660.0530.1070.0860.0760.1100.3510.0490.0500.0301.0000.0460.0320.0020.0190.0370.0380.0330.0440.0330.0550.0350.0190.0330.0420.0360.0300.0520.0290.0540.0400.0450.048
town_bukit merah0.0150.0850.2450.9130.4400.1210.1000.2000.0580.2160.0090.0140.4840.6660.1420.0840.4300.1020.0910.1320.1320.0580.0590.0360.0461.0000.0390.0040.0230.0450.0460.0400.0520.0400.0660.0410.0230.0400.0510.0430.0360.0630.0340.0640.0470.0540.057
town_bukit panjang0.0050.0860.1860.2690.3480.0270.1220.2710.0590.1040.0000.0040.2250.4940.0820.0690.0910.0720.0640.0930.2960.0410.0420.0250.0320.0391.0000.0000.0160.0310.0320.0280.0370.0280.0470.0290.0160.0280.0360.0300.0250.0440.0240.0450.0330.0380.040
town_bukit timah0.0000.0180.0950.0670.1150.0160.0460.0500.0360.0460.0000.0080.0410.0860.0240.0140.0550.0120.0100.0160.0160.0050.0050.0000.0020.0040.0001.0000.0000.0020.0020.0000.0040.0000.0060.0000.0000.0000.0030.0010.0000.0060.0000.0060.0030.0040.005
town_central0.0000.0720.1290.2640.1610.1280.1520.1050.1790.1260.0110.0091.0000.2850.1060.0440.1840.0440.0390.0560.0560.0240.0250.0150.0190.0230.0160.0001.0000.0180.0190.0160.0220.0160.0280.0170.0080.0160.0210.0180.0150.0260.0140.0270.0200.0230.024
town_choa chu kang0.0170.1380.2260.2980.4340.0330.0570.1650.0640.1100.0080.0000.2271.0000.1450.1140.1050.0830.0740.1080.3410.0470.0480.0300.0370.0450.0310.0020.0181.0000.0370.0320.0430.0320.0540.0340.0180.0320.0410.0350.0300.0510.0280.0520.0390.0440.046
town_clementi0.0120.1330.3510.4690.4340.0270.0780.2250.1780.1220.0110.0000.2340.5870.2520.1300.1080.0860.0770.1110.3520.0490.0500.0300.0380.0460.0320.0020.0190.0371.0000.0330.0440.0330.0550.0350.0190.0330.0430.0360.0310.0530.0290.0540.0400.0450.048
town_geylang0.0000.1270.2320.3490.3780.0380.1700.2460.1030.2280.0000.0000.3531.0000.2570.0990.3140.0740.0660.0960.0960.0420.0430.0260.0330.0400.0280.0000.0160.0320.0331.0000.0380.0290.0480.0300.0160.0290.0370.0310.0260.0450.0250.0470.0340.0390.041
town_hougang0.0030.0480.1590.3890.4400.0250.1270.1620.0720.1120.0080.0000.2660.6590.0620.0440.1220.0970.0870.3990.1260.0550.0570.0350.0440.0520.0370.0040.0220.0430.0440.0381.0000.0380.0630.0400.0220.0380.0480.0410.0350.0600.0330.0610.0450.0520.054
town_jurong east0.0060.0360.1480.2350.4720.0020.1010.2240.3470.2020.0000.0000.2320.3150.0700.0230.0940.0740.0660.0960.3050.0420.0430.0260.0330.0400.0280.0000.0160.0320.0330.0290.0381.0000.0480.0300.0160.0290.0370.0310.0260.0450.0250.0470.0340.0390.041
town_jurong west0.0100.1240.1650.4040.9020.0360.0800.2400.0850.1020.0000.0000.3350.6750.1280.1140.1540.1230.1100.1580.5030.0700.0710.0440.0550.0660.0470.0060.0280.0540.0550.0480.0630.0481.0000.0500.0280.0480.0610.0520.0440.0750.0420.0770.0570.0650.069
town_kallang/whampoa0.0000.1100.2160.3960.4380.0220.1350.2860.2130.2000.0000.0001.0000.6470.1350.0900.3260.0770.0690.1000.1000.0440.0450.0270.0350.0410.0290.0000.0170.0340.0350.0300.0400.0300.0501.0000.0170.0300.0380.0330.0270.0470.0260.0490.0360.0410.043
town_marine parade0.0000.1170.3670.4120.2730.0140.5100.1330.0640.0380.0000.0000.1200.2970.1380.0620.1850.0440.0390.0560.0560.0240.0250.0150.0190.0230.0160.0000.0080.0180.0190.0160.0220.0160.0280.0171.0000.0160.0210.0180.0150.0260.0140.0270.0200.0230.024
town_pasir ris0.0150.2410.3440.3760.4520.0360.1900.2200.0830.0790.0100.0070.2320.3160.2000.1660.0940.3950.0670.0960.0960.0420.0430.0260.0330.0400.0280.0000.0160.0320.0330.0290.0380.0290.0480.0300.0161.0000.0370.0320.0260.0460.0250.0470.0340.0390.042
town_punggol0.0190.1630.3560.5440.4000.0520.2320.2390.1470.1260.0160.0090.2940.3990.1620.1430.1190.0940.0840.3870.1220.0540.0550.0340.0420.0510.0360.0030.0210.0410.0430.0370.0480.0370.0610.0380.0210.0371.0000.0400.0340.0580.0320.0590.0440.0500.053
town_queenstown0.0070.1270.3300.5850.4390.0910.0750.4090.2090.0680.0100.0050.2520.5270.2470.0990.3410.0810.0720.1050.1050.0460.0470.0290.0360.0430.0300.0010.0180.0350.0360.0310.0410.0310.0520.0330.0180.0320.0401.0000.0290.0500.0270.0510.0370.0430.045
town_sembawang0.0100.1270.2610.6840.4500.0270.0850.9710.1160.0740.0100.0060.2120.3760.1230.1230.0860.0680.4040.0880.0880.0390.0390.0240.0300.0360.0250.0000.0150.0300.0310.0260.0350.0260.0440.0270.0150.0260.0340.0291.0000.0420.0230.0430.0310.0360.038
town_sengkang0.0070.1900.3820.6970.5170.0480.2510.3030.1010.1720.0000.0030.3170.7550.1780.1730.1460.1160.1040.4770.1500.0660.0680.0420.0520.0630.0440.0060.0260.0510.0530.0450.0600.0450.0750.0470.0260.0460.0580.0500.0421.0000.0390.0730.0540.0620.065
town_serangoon0.0070.0470.1870.2140.4260.0290.2030.0950.0930.0730.0000.0000.1760.4370.0800.0400.0810.0640.0570.2650.0830.0360.0370.0230.0290.0340.0240.0000.0140.0280.0290.0250.0330.0250.0420.0260.0140.0250.0320.0270.0230.0391.0000.0400.0300.0340.036
town_tampines0.0000.1350.2990.5210.6830.0380.1070.2850.0590.0720.0000.0000.5650.5040.1260.0350.1500.6300.1060.1540.1540.0680.0690.0430.0540.0640.0450.0060.0270.0520.0540.0470.0610.0470.0770.0490.0270.0470.0590.0510.0430.0730.0401.0000.0550.0630.067
town_toa payoh0.0070.1420.3280.4540.3160.0230.1230.5010.0600.0950.0000.0090.4190.7380.2010.1210.3720.0880.0790.1140.1140.0500.0510.0310.0400.0470.0330.0030.0200.0390.0400.0340.0450.0340.0570.0360.0200.0340.0440.0370.0310.0540.0300.0551.0000.0470.049
town_woodlands0.0000.1640.2460.6350.6060.0440.0710.5870.0670.1600.0050.0000.3130.5530.1150.1100.1260.1010.5950.1300.1300.0570.0580.0360.0450.0540.0380.0040.0230.0440.0450.0390.0520.0390.0650.0410.0230.0390.0500.0430.0360.0620.0340.0630.0471.0000.056
town_yishun0.0060.0920.2980.7350.5260.0750.0500.7560.0940.0940.0100.0040.3300.4480.1460.0540.1330.1060.6280.1370.1370.0600.0620.0380.0480.0570.0400.0050.0240.0460.0480.0410.0540.0410.0690.0430.0240.0420.0530.0450.0380.0650.0360.0670.0490.0561.000

Missing values

2023-11-07T06:00:53.246114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T06:00:54.596109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

rent_approval_dateflat_typefloor_area_sqmlease_commence_datelatitudelongitudemonthly_rentdistance_to_nearest_existing_mrtdistance_to_nearest_planned_mrtdistance_to_nearest_schooldistance_to_nearest_mallcoe_price_indicatorstock_pricetown_importancetown_centrality_page_rankflat_type_modelregion_central regionregion_east regionregion_north regionregion_north-east regionregion_west regiontown_ang mo kiotown_bedoktown_bishantown_bukit batoktown_bukit merahtown_bukit panjangtown_bukit timahtown_centraltown_choa chu kangtown_clementitown_geylangtown_hougangtown_jurong easttown_jurong westtown_kallang/whampoatown_marine paradetown_pasir ristown_punggoltown_queenstowntown_sembawangtown_sengkangtown_serangoontown_tampinestown_toa payohtown_woodlandstown_yishun
00.2667400.250.1823200.3207551.344518103.73863016000.2719000.0678480.1433550.5161030.1775700.7964430.0422540.0383230.342105FalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
10.5323820.500.3204420.2264151.330186103.93871722500.3538660.0922390.2772860.4781960.3926780.1073460.1338030.0384140.605263FalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
20.7003290.250.1823200.0943401.332242103.84564319000.0748310.3914390.1879770.2009600.6623420.0000000.2816900.0388190.210526TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
30.2327111.000.6353590.5094341.370239103.96289428500.6192290.0509440.2563040.1726640.1383800.7949250.0985920.0382980.921053FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse
40.7343580.250.1878450.1132081.320502103.86334121000.0622210.2972980.1123730.4606080.7502920.0899780.5563380.0387990.210526TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
50.9001101.000.5303870.6603771.387847103.76424923000.3879640.2341450.1163050.1523840.8482780.0074250.0704230.0384020.842105FalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
60.0000000.750.4198900.7358491.388997103.87514821000.8363920.2136210.0598810.1268730.0313660.3428920.1478870.0384800.657895FalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalse
70.5664110.250.1823200.2264151.366048103.83812323000.2420950.0437260.3869910.4673000.5329080.1463580.0492960.0385580.342105FalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
80.2996710.500.2762430.3962261.344279103.85555621000.3817810.2854910.1949380.4431340.3155431.0000000.1830990.0386410.447368TrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
90.0987930.750.4309390.6981131.392832103.91062021000.6417260.0712260.1266050.1353800.1839800.6193150.0915490.0383270.657895FalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
rent_approval_dateflat_typefloor_area_sqmlease_commence_datelatitudelongitudemonthly_rentdistance_to_nearest_existing_mrtdistance_to_nearest_planned_mrtdistance_to_nearest_schooldistance_to_nearest_mallcoe_price_indicatorstock_pricetown_importancetown_centrality_page_rankflat_type_modelregion_central regionregion_east regionregion_north regionregion_north-east regionregion_west regiontown_ang mo kiotown_bedoktown_bishantown_bukit batoktown_bukit merahtown_bukit panjangtown_bukit timahtown_centraltown_choa chu kangtown_clementitown_geylangtown_hougangtown_jurong easttown_jurong westtown_kallang/whampoatown_marine paradetown_pasir ristown_punggoltown_queenstowntown_sembawangtown_sengkangtown_serangoontown_tampinestown_toa payohtown_woodlandstown_yishun
594670.0647640.750.4917130.4150941.343110103.94956025000.1578870.1692890.1877720.1664140.0000000.6780640.2253520.0383020.736842FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
594680.1657520.500.3259670.8301891.403778103.90576228000.1570150.0384990.0606530.2118110.1279260.7512750.0915490.0383270.526316FalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
594690.8013170.500.3204420.2641511.308218103.75894338000.4095800.0291900.2820760.4068480.7116370.0205850.1549300.0384030.605263FalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
594700.9330410.750.4972380.6226421.401916103.74618841000.1952900.1342050.0408240.1895030.9014620.1395330.1056340.0382350.657895FalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
594710.8353460.250.1878450.9433961.316595103.87954629500.1411830.2323030.2198460.6307910.9181480.0536860.2676060.0387290.263158TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
594720.2667400.250.1823200.2452831.366050103.85416822000.2599930.1080470.1560200.4075530.1775700.7964430.0492960.0385580.342105FalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
594730.9001100.500.2707181.0000001.286493103.82143441000.2392720.2210820.2551860.3178450.8482780.0074250.2957750.0386020.552632TrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
594740.5664110.750.4861880.4150941.355064103.93650722500.2816600.1639120.1360830.1887290.5329080.1463580.2253520.0383020.736842FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
594750.8013170.750.4917130.2075471.318974103.94407647000.3639340.0505370.0867640.7617900.7116370.0205850.1338030.0384140.684211FalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
594760.8353460.250.1823200.2452831.366980103.85571828500.2966240.0925160.2496550.4403450.9181480.0536860.0492960.0385580.342105FalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse